

'''simple docstring'''





# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


A_	= "3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
				import torch

				print("Torch version:", torch.__version__)
				print("Cuda available:", torch.cuda.is_available())
				print("Cuda version:", torch.version.cuda)
				print("CuDNN version:", torch.backends.cudnn.version())
				print("Number of GPUs available:", torch.cuda.device_count())
				print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
				print("Torch version:", None)

try:
				import deepspeed

				print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
				print("DeepSpeed version:", None)

try:
				import tensorflow as tf

				print("TensorFlow version:", tf.__version__)
				print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
				print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
				print("TensorFlow version:", None)




"""simple docstring"""



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


SCREAMING_SNAKE_CASE__:Any									=			get_logger(__name__)


SCREAMING_SNAKE_CASE__:List[Any]									=			Path(__file__).parent / """model_card_template.md"""
SCREAMING_SNAKE_CASE__:str									=			uuida().hex
SCREAMING_SNAKE_CASE__:Optional[Any]									=			os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
SCREAMING_SNAKE_CASE__:List[Any]									=			os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
SCREAMING_SNAKE_CASE__:Optional[Any]									=			HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""



def 	_lowerCamelCase(	a = None				):
       __a       =    F"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
       if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
              return ua + "; telemetry/off"
       if is_torch_available():
              ua += F"; torch/{_torch_version}"
       if is_flax_available():
              ua += F"; jax/{_jax_version}"
              ua += F"; flax/{_flax_version}"
       if is_onnx_available():
              ua += F"; onnxruntime/{_onnxruntime_version}"
       # CI will set this value to True
       if os.environ.get("DIFFUSERS_IS_CI"   ,    ""				).upper() in ENV_VARS_TRUE_VALUES:
              ua += "; is_ci/true"
       if isinstance(a   ,    a				):
              ua += "; " + "; ".join(F"{k}/{v}" for k, v in user_agent.items()				)
       elif isinstance(a   ,    a				):
              ua += "; " + user_agent
       return ua



def 	_lowerCamelCase(	a   ,    a = None   ,    a = None				):
       if token is None:
              __a       =    HfFolder.get_token()
       if organization is None:
              __a       =    whoami(a				)["name"]
              return F"{username}/{model_id}"
       else:
              return F"{organization}/{model_id}"



def 	_lowerCamelCase(	a   ,    a				):
       if not is_jinja_available():
              raise ValueError(
                  "Modelcard rendering is based on Jinja templates."
                  " Please make sure to have `jinja` installed before using `create_model_card`."
                  " To install it, please run `pip install Jinja2`."				)

       if hasattr(a   ,    "local_rank"				) and args.local_rank not in [-1, 0]:
              return

       __a       =    args.hub_token if hasattr(a   ,    "hub_token"				) else None
       __a       =    get_full_repo_name(a   ,    token=a				)

       __a       =    ModelCard.from_template(
           card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
               language="en"   ,    license="apache-2.0"   ,    library_name="diffusers"   ,    tags=[]   ,    datasets=args.dataset_name   ,    metrics=[]   ,    )   ,    template_path=a   ,    model_name=a   ,    repo_name=a   ,    dataset_name=args.dataset_name if hasattr(a   ,    "dataset_name"				) else None   ,    learning_rate=args.learning_rate   ,    train_batch_size=args.train_batch_size   ,    eval_batch_size=args.eval_batch_size   ,    gradient_accumulation_steps=(
               args.gradient_accumulation_steps if hasattr(a   ,    "gradient_accumulation_steps"				) else None
           )   ,    adam_betaa=args.adam_betaa if hasattr(a   ,    "adam_beta1"				) else None   ,    adam_betaa=args.adam_betaa if hasattr(a   ,    "adam_beta2"				) else None   ,    adam_weight_decay=args.adam_weight_decay if hasattr(a   ,    "adam_weight_decay"				) else None   ,    adam_epsilon=args.adam_epsilon if hasattr(a   ,    "adam_epsilon"				) else None   ,    lr_scheduler=args.lr_scheduler if hasattr(a   ,    "lr_scheduler"				) else None   ,    lr_warmup_steps=args.lr_warmup_steps if hasattr(a   ,    "lr_warmup_steps"				) else None   ,    ema_inv_gamma=args.ema_inv_gamma if hasattr(a   ,    "ema_inv_gamma"				) else None   ,    ema_power=args.ema_power if hasattr(a   ,    "ema_power"				) else None   ,    ema_max_decay=args.ema_max_decay if hasattr(a   ,    "ema_max_decay"				) else None   ,    mixed_precision=args.mixed_precision   ,    )

       __a       =    os.path.join(args.output_dir   ,    "README.md"				)
       model_card.save(a				)



def 	_lowerCamelCase(	a   ,    a = None				):
       if resolved_file is None or commit_hash is not None:
              return commit_hash
       __a       =    str(Path(a				).as_posix()				)
       __a       =    re.search(R"snapshots/([^/]+)/"   ,    a				)
       if search is None:
              return None
       __a       =    search.groups()[0]
       return commit_hash if REGEX_COMMIT_HASH.match(a				) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
SCREAMING_SNAKE_CASE__:str									=			os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
SCREAMING_SNAKE_CASE__:List[Any]									=			os.path.join(hf_cache_home, """diffusers""")



def 	_lowerCamelCase(	a = None   ,    a = None				):
       if new_cache_dir is None:
              __a       =    DIFFUSERS_CACHE
       if old_cache_dir is None:
              __a       =    old_diffusers_cache

       __a       =    Path(a				).expanduser()
       __a       =    Path(a				).expanduser()
       for old_blob_path in old_cache_dir.glob("**/blobs/*"				):
              if old_blob_path.is_file() and not old_blob_path.is_symlink():
                     __a       =    new_cache_dir / old_blob_path.relative_to(a				)
                     new_blob_path.parent.mkdir(parents=a   ,    exist_ok=a				)
                     os.replace(a   ,    a				)
                     try:
                            os.symlink(a   ,    a				)
                     except OSError:
                            logger.warning(
                                "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."				)
       # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


SCREAMING_SNAKE_CASE__:Dict									=			os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
   SCREAMING_SNAKE_CASE__:Optional[int]									=			0
else:
   with open(cache_version_file) as f:
      try:
         SCREAMING_SNAKE_CASE__:List[str]									=			int(f.read())
      except ValueError:
         SCREAMING_SNAKE_CASE__:List[str]									=			0

if cache_version < 1:
   SCREAMING_SNAKE_CASE__:List[str]									=			os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
   if old_cache_is_not_empty:
      logger.warning(
          """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
          """existing cached models. This is a one-time operation, you can interrupt it or run it """
          """later by calling `diffusers.utils.hub_utils.move_cache()`."""
      )
      try:
         move_cache()
      except Exception as e:
         SCREAMING_SNAKE_CASE__:Optional[int]									=			"""\n""".join(traceback.format_tb(e.__traceback__))
         logger.error(
             F'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
             """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
             """message and we will do our best to help."""
         )

if cache_version < 1:
   try:
      os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
      with open(cache_version_file, """w""") as f:
         f.write("""1""")
   except Exception:
      logger.warning(
          F'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
          """the directory exists and can be written to."""
      )



def 	_lowerCamelCase(	a   ,    a = None				):
       if variant is not None:
              __a       =    weights_name.split("."				)
              __a       =    splits[:-1] + [variant] + splits[-1:]
              __a       =    ".".join(a				)

       return weights_name




def 	_lowerCamelCase(	a   ,    *,
    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a=None   ,    ):
       __a       =    str(a				)
       if os.path.isfile(a				):
              return pretrained_model_name_or_path
       elif os.path.isdir(a				):
              if os.path.isfile(os.path.join(a   ,    a				)				):
                     # Load from a PyTorch checkpoint
                     __a       =    os.path.join(a   ,    a				)
                     return model_file
              elif subfolder is not None and os.path.isfile(
                  os.path.join(a   ,    a   ,    a				)				):
                     __a       =    os.path.join(a   ,    a   ,    a				)
                     return model_file
              else:
                     raise EnvironmentError(
                         F"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."				)
       else:
              # 1. First check if deprecated way of loading from branches is used
              if (
                  revision in DEPRECATED_REVISION_ARGS
                  and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
                  and version.parse(version.parse(a				).base_version				) >= version.parse("0.20.0"				)
              ):
                     try:
                            __a       =    hf_hub_download(
                                a   ,    filename=_add_variant(a   ,    a				)   ,    cache_dir=a   ,    force_download=a   ,    proxies=a   ,    resume_download=a   ,    local_files_only=a   ,    use_auth_token=a   ,    user_agent=a   ,    subfolder=a   ,    revision=revision or commit_hash   ,    )
                            warnings.warn(
                                F"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."   ,    a   ,    )
                            return model_file
                     except:  # noqa: E722
                            warnings.warn(
                                F"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a   ,    a				)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a   ,    a				)}' so that the correct variant file can be added."   ,    a   ,    )
              try:
                     # 2. Load model file as usual
                     __a       =    hf_hub_download(
                         a   ,    filename=a   ,    cache_dir=a   ,    force_download=a   ,    proxies=a   ,    resume_download=a   ,    local_files_only=a   ,    use_auth_token=a   ,    user_agent=a   ,    subfolder=a   ,    revision=revision or commit_hash   ,    )
                     return model_file

              except RepositoryNotFoundError:
                     raise EnvironmentError(
                         F"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                         "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                         "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
                         "login`."				)
              except RevisionNotFoundError:
                     raise EnvironmentError(
                         F"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
                         "this model name. Check the model page at "
                         F"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."				)
              except EntryNotFoundError:
                     raise EnvironmentError(
                         F"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."				)
              except HTTPError as err:
                     raise EnvironmentError(
                         F"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"				)
              except ValueError:
                     raise EnvironmentError(
                         F"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                         F" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                         F" directory containing a file named {weights_name} or"
                         " \nCheckout your internet connection or see how to run the library in"
                         " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."				)
              except EnvironmentError:
                     raise EnvironmentError(
                         F"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                         "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                         F"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                         F"containing a file named {weights_name}"				)
